Asynchronously Trained Distributed Topographic Maps

Topographic feature maps are low dimensional representations of data, that preserve spatial dependencies. Current methods of training such maps (e.g. self organizing maps - SOM, generative topographic maps) require centralized control and synchronous execution, which restricts scalability. We present an algorithm that uses $N$ autonomous units to generate a feature map by distributed asynchronous training. Unit autonomy is achieved by sparse interaction in time \& space through the combination of a distributed heuristic search, and a cascade-driven weight updating scheme governed by two rules: a unit i) adapts when it receives either a sample, or the weight vector of a neighbor, and ii) broadcasts its weight vector to its neighbors after adapting for a predefined number of times. Thus, a vector update can trigger an avalanche of adaptation. We map avalanching to a statistical mechanics model, which allows us to parametrize the statistical properties of cascading. Using MNIST, we empirically investigate the effect of the heuristic search accuracy and the cascade parameters on map quality. We also provide empirical evidence that algorithm complexity scales at most linearly with system size $N$. The proposed approach is found to perform comparably with similar methods in classification tasks across multiple datasets.Comment: 11 Pages, 8 Figures

On 2-strong connectivity orientations of mixed graphs and related problems

A mixed graph $G$ is a graph that consists of both undirected and directed edges. An orientation of $G$ is formed by orienting all the undirected edges of $G$, i.e., converting each undirected edge $\{u,v\}$ into a directed edge that is either $(u,v)$ or $(v,u)$. The problem of finding an orientation of a mixed graph that makes it strongly connected is well understood and can be solved in linear time. Here we introduce the following orientation problem in mixed graphs. Given a mixed graph $G$, we wish to compute its maximal sets of vertices $C_1,C_2,\ldots,C_k$ with the property that by removing any edge $e$ from $G$ (directed or undirected), there is an orientation $R_i$ of $G\setminus{e}$ such that all vertices in $C_i$ are strongly connected in $R_i$. We discuss properties of those sets, and we show how to solve this problem in linear time by reducing it to the computation of the $2$-edge twinless strongly connected components of a directed graph. A directed graph $G=(V,E)$ is twinless strongly connected if it contains a strongly connected spanning subgraph without any pair of antiparallel (or twin) edges. The twinless strongly connected components (TSCCs) of a directed graph $G$ are its maximal twinless strongly connected subgraphs. A $2$-edge twinless strongly connected component (2eTSCC) of $G$ is a maximal subset of vertices $C$ such that any two vertices $u, v \in C$ are in the same twinless strongly connected component of $G \setminus e$, for any edge $e$. These concepts are motivated by several diverse applications, such as the design of road and telecommunication networks, and the structural stability of buildings

AN AR INDOOR POSITIONING SYSTEM BASED ON ANCHORS

Indoor navigation is a very interesting scientific domain due to its potential use compared with the GPS signals, which are restricted to outdoor environments. This paper describes commonly used methods of Indoor navigation, positioning, and mapping systems using Augmented Reality (AR) techniques. An Indoor navigation system, which is based on an AR application, is a pipelined procedure, which is consisted of three modules. Those are the positioning system, the map, and the route planning algorithms. In this paper, the emphasis is placed on the positioning system module and the creation of the map. The most notable options concerning the AR positioning systems use markers or detected planes in the environment in order to accurately define the position of the user in it. In this paper, we propose a new method of positioning which is based on anchors and unlike other methods can provide a total marker-less experience to the user. Anchors are a crucial feature of most AR Frameworks and used to add augmented content on top of a feature point. Also, we propose a mapping technique that fully supports the positioning method mentioned previously. Concepts like AR Frameworks, anchors, and feature points are also, deeply discussed. The proposed method for position tracking does not require any special hardware or component other than a smartphone with a camera. The proposed method for map creation is an enhanced version of an existing method of the ARKit framework. Finally, the paper analyzes the new methods in terms of accuracy in the estimated user position and measures the error in a distance calculation module that was developed to support the positioning method

Co-Movement Analysis of Italian and Greek Electricity Market Wholesale Prices by Using a Wavelet Approach

We study the co-evolution of the dynamics or co-movement of two electricity markets, the Italian and Greek, by studying the dynamics of their wholesale day-ahead prices, simultaneously in the time-frequency domain. Co-movement is alternatively referred as market integration in financial economics and markets are internationally integrated if the reward for risk is identical regardless the market one trades in. The innovation of this work is the application of wavelet analysis and more specifically the wavelet coherence to estimate the dynamic interaction between these two prices. Our method is compared to other generic econometric tools used in Economics and Finance namely the dynamic correlation and coherence analysis, to study the co-movement of variables of the type related to these two fields. Our study reveals valuable information that we believe will be extremely useful to the authorities as well as other agents participating in these markets to better prepare the national markets towards the European target model, a framework in which the two markets will be coupled